Artificial intelligence has transformed the receivables industry, but the rapid pace of change has created confusion around terminology. Are we even using the right language when evaluating AI tools?

Large language models like ChatGPT aren’t thinking brains – they’re sophisticated statistical systems predicting likely word sequences based on training data. This distinction matters when choosing solutions for your operation. Meanwhile, machine learning for propensity-to-pay scoring, once cutting-edge, has become table stakes.

Several key concepts deserve attention from collections professionals. Model Context Protocol (MCP) servers act as security gateways between AI tools and your system of record, preventing unauthorized actions while future-proofing your tech stack against rapid changes in the AI landscape. Retrieval Augmented Generation (RAG) allows AI systems to reference your compliance documents, training materials, and regulations – giving context that generic models lack.

One persistent misconception: AI systems aren’t constantly learning from your conversations in real-time. Context windows create that appearance, but actual model training happens offline through iterative processes. When vendors claim otherwise, ask harder questions.

The real power emerges when these components work together in an integrated ecosystem. Combining dialers, CRM, speech analytics with tone detection, and AI co-pilots delivers results that siloed solutions simply cannot match. Tight integration enables real-time transcription, sentiment analysis, and automated agent assistance all flowing from the same data source.

For receivables professionals evaluating AI solutions, understanding these foundational concepts separates informed decisions from expensive mistakes.

Watch the full conversation above for deeper insights into each of these topics.